The present invention claims priority from Japanese Patent Application No. 10-321378 filed Oct. 28, 1999, the contents of which are incorporated herein by reference.
1. Field of the Invention
The present invention relates to a brain wave data processing device used for processing brain wave data to detect distinctive patterns, and more particularly to a brain wave data processing device which averages brain wave data in which distinguishing patterns are detected in individual brain wave data obtained in a single trial, and to a computer readable storage medium in which a program to realize this brain wave data processing device is loaded.
2. Description of Related Art
When an excitation or event is added to a subject (trial), an electric potential evoked by the added excitation or event (evoked potential) would occur in the brain and spinal cord of the subject. In particular, a potential variation occurred in a cerebral cortex with a constant time relation to an excitation or event is referred to as an event-related potential (ERP). Such a potential or potential variation is generally observed as brain wave data, and used as effective data to know the mechanism of, for example, feeling, perception, and psychological phenomenon, or to find a position of an injury. However, the amplitude of the potential variations involved with the trial is considerably smaller than that of rhythmic, ordinary brain waves, so that a signal occurred from one observation is often indistinct. Therefore, a method has been devised in which multiple trials are performed under the same conditions and the brain wave data obtained from each trial are added and averaged with the time of excitation (event) as a reference point, causing components of the rhythmic, ordinary brain waves to be canceled, so that only the evoked potential obtained by the excitation (event) is extracted. This technique is referred to as an averaging method.
Incidentally, as a technique to process an unsteady signal which changes over time to analyze whether or not a distinguishing component is included, in these days, attention has focused on the wavelet conversion. An example of the brain wave data subjected to the wavelet conversion in a single trial includes the reference xe2x80x9cP300 Single Trial Storage Processing Based on Wavelet Conversionxe2x80x9d by Masatoshi Nakamura, Yasushi Hisatomi, Naoshi Sugikou, Shigeto Nishida, Yoshio Ikeda, and Hiroshi Shibasaki (Proceedings of the fifteenth SICE Kushu branch congress, pp. 355-358, Nov. 23, 1996). However, the device by Nakamura et al. provides only the wavelet conversion and inverse wavelet conversion with respect to the brain wave data, and includes only the filtering function to eliminate noises in live data. Further, although they have attempted to restrict parameters included in the wavelet conversion according to the result of filtering to the live data, the reliability of the result can not be expected, because they use a formula model as a true waveform. In addition, they have examined wavelet conversion parameters only in limited ranges, so that a considerable amount of information contained in the live data may have been lost. The largest problem of the report by Nakamura et al. is that how to use the waveforms subjected to filtering process (the waveform data subjected to the wavelet conversion and inverse wavelet conversion) is not mentioned. After all, the device by Nakamura et al. may be considered not to reach the practical stage yet, although the attempt to analyze the brain wave data using the wavelet conversion and inverse wavelet conversion can be found.
When the averaging of the brain wave data is determined by the averaging method and the evoked potential, such as the event-related potential (ERP), is observed, in the past, an enormous period of time was required in order to extract distinguishing patterns from individual brain wave data to obtain the averaging of the brain wave data. The reason for it is that the work of extracting the patterns was all performed by the inspection of an experimenter (or a decipherer of the brain wave).
It is an object of the present invention to provide a method for analyzing brain wave data which can automate the work to extract distinguishing patterns from brain wave to reduce the load of the experimenter and improve the quality and reliability of the brain wave data obtained as well as the efficiency of the work for analyzing the brain wave data.
In addition, as described above, in the past, a concrete application of the wavelet conversion for the brain wave data was not made clear, however, it is also another object of the present invention to exhibit concrete applications. Accordingly, the present invention exhibits the averaging of the brain wave data as a concrete application of the wavelet conversion, and also it is an object of the present invention to provide a device which performs the averaging without significantly losing the information of original waveform data by considering not only the waveform data itself as with the prior art but also whole of values of the wavelet conversion parameters, when the results of the wavelet conversion are compared and examined.
The brain wave data processing device according to the present invention comprises, in a brain wave data processing device detecting distinguishing patterns from individual brain wave data obtained in a single trial, a brain wave data storage means for storing digital brain wave data, a wavelet conversion means for subjecting the digital brain wave data read out from the brain wave data storage means to wavelet conversion to determine a wavelet coefficient, a wavelet coefficient surface output means for outputting the wavelet coefficient as function values of a scale parameter and a shift parameter in the wavelet conversion, a wavelet coefficient window parameter setting means for setting a wavelet coefficient window, a wavelet coefficient window means for extracting a predetermined area based on the wavelet coefficient window from a wavelet coefficient surface defined by the scale parameter, shift parameter, and wavelet coefficient, and a brain wave data discriminating means for discriminating whether or not the predetermined area has been extracted from the wavelet coefficient surface by the wavelet coefficient window means for individual digital brain wave data.
The brain wave data processing device according to the present invention may further be provided with a brain wave data averaging means for averaging only the digital brain wave data from which the predetermined area is extracted in the wavelet coefficient surface and a pattern latency extraction means for determining a vertex latency of distinguishing patterns included only in the digital brain wave data from which the predetermined area is extracted in the wavelet coefficient surface.
In the present invention, the brain wave data in which the distinguishing patterns have been detected, for example, by inspection are previously prepared and the corresponding wavelet coefficient surface is determined from these brain wave data, and the wavelet coefficient window may be set according to the shape and value of this wavelet coefficient surface. Although various types are considered as a mother wavelet in the wavelet conversion, Mexican Hat can be exhibited as a desirable one.
According to the present invention, the wavelet coefficient surface is the result of subjecting the brain wave data to the wavelet conversion, and by subjecting this wavelet coefficient surface to the wavelet coefficient window, it is discriminated whether or not a predetermined area is extracted in the wavelet coefficient surface, so that all the processing from the measurement of the brain wave data to the discrimination of whether distinguishing patterns exist in the brain wave data can be automatically performed.
Furthermore, by providing a brain wave data averaging means, all the processing from the measurement of the brain wave data to the averaging process can be automatically executed, and by providing a pattern latency extraction means, a vertex latency of the extracted pattern can be automatically determined.